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Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models

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  • Jaydip Sen
  • Sidra Mehtab

Abstract

Designing robust frameworks for precise prediction of future prices of stocks has always been considered a very challenging research problem. The advocates of the classical efficient market hypothesis affirm that it is impossible to accurately predict the future prices in an efficiently operating market due to the stochastic nature of the stock price variables. However, numerous propositions exist in the literature with varying degrees of sophistication and complexity that illustrate how algorithms and models can be designed for making efficient, accurate, and robust predictions of stock prices. We present a gamut of ten deep learning models of regression for precise and robust prediction of the future prices of the stock of a critical company in the auto sector of India. Using a very granular stock price collected at 5 minutes intervals, we train the models based on the records from 31st Dec, 2012 to 27th Dec, 2013. The testing of the models is done using records from 30th Dec, 2013 to 9th Jan 2015. We explain the design principles of the models and analyze the results of their performance based on accuracy in forecasting and speed of execution.

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  • Jaydip Sen & Sidra Mehtab, 2021. "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models," Papers 2103.15096, arXiv.org.
    • Handle: RePEc:arx:papers:2103.15096
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    References listed on IDEAS

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    1. Manish Kumar & M. Thenmozhi, 2014. "Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 5(3), pages 284-308.
    2. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    3. Muhammad Zubair Asghar & Fazal Rahman & Fazal Masud Kundi & Shakeel Ahmad, 2019. "Development of stock market trend prediction system using multiple regression," Computational and Mathematical Organization Theory, Springer, vol. 25(3), pages 271-301, September.
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    Cited by:

    1. Jaydip Sen & Subhasis Dasgupta, 2023. "Portfolio Optimization: A Comparative Study," Papers 2307.05048, arXiv.org.
    2. Jaydip Sen & Rajdeep Sen & Abhishek Dutta, 2021. "Machine Learning in Finance-Emerging Trends and Challenges," Papers 2110.11999, arXiv.org.
    3. Jaydip Sen & Sidra Mehtab, 2021. "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction," Papers 2106.09664, arXiv.org.
    4. Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021. "Stock Portfolio Optimization Using a Deep Learning LSTM Model," Papers 2111.04709, arXiv.org.
    5. Jaydip Sen & Ashwin Kumar R S & Geetha Joseph & Kaushik Muthukrishnan & Koushik Tulasi & Praveen Varukolu, 2022. "Precise Stock Price Prediction for Robust Portfolio Design from Selected Sectors of the Indian Stock Market," Papers 2201.05570, arXiv.org.

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